Parallel Systems
Early Career Research Engineer
Palo Alto
Sponsorship not specifiedDetected 179 days ago
Deep LearningSparkLLMsAgentic AIResearch
About the role
- This means tackling research problems that most labs encounter only at hyperscale: How do you train embedding models that capture semantic intent across diverse query types?
- How do you balance model expressiveness with sub-second retrieval latency?
- This is information retrieval reimagined for the LLM era, work that combines classical IR techniques with modern deep learning, applied at a scale that demands new solutions.
Responsibilities
- How do you maintain index freshness when the web updates constantly, without rebuilding from scratch?
- Unlike traditional search engines built for human queries, you're building for AI agents that issue complex, multi-hop queries and expect structured, programmatic responses.
- You'll design and train the models that power Parallel's APIs: the intelligence layer that helps AI agents find exactly what they need from the open web.
Compensation
- Competitive salary
Benefits
- Generous equity
- Unlimited vacation
- Caltrain pass reimbursement
Company info
- Parallel is a web infrastructure company. Our products are used by leading businesses in sales, marketing, insurance, and coding to build best-in-class AI agents with flexible and powerful programmatic access to the web.
- We've raised $230 million from Kleiner Perkins, Sequoia, Index Ventures, Spark Capital, Khosla Ventures, First Round, and Terrain to build the web for AIs. We're currently valued at $2 billion and we're forming a world-class team of engineers, designers, marketers, sellers, researchers, and operational experts to achieve our mission.
- You're a researcher who thinks like an engineer, or an engineer who thinks like a researcher. You've worked on information retrieval systems, embedding models, or neural ranking at scale, or you're deeply curious about the fundamental problems that emerge when training models to understand and serve billions of web documents. You thrive in the space between theory and production, where elegant solutions must also run efficiently on real infrastructure. You're comfortable reading papers from SIGIR and RecSys one day and debugging distributed training pipelines the next.
- Parallel is a web infrastructure company.
- Our products are used by leading businesses in sales, marketing, insurance, and coding to build best-in-class AI agents with flexible and powerful programmatic access to the web.
- We've raised $230 million from Kleiner Perkins, Sequoia, Index Ventures, Spark Capital, Khosla Ventures, First Round, and Terrain to build the web for AIs.
- We're currently valued at $2 billion and we're forming a world-class team of engineers, designers, marketers, sellers, researchers, and operational experts to achieve our mission.
- You're a researcher who thinks like an engineer, or an engineer who thinks like a researcher.
- You've worked on information retrieval systems, embedding models, or neural ranking at scale, or you're deeply curious about the fundamental problems that emerge when training models to understand and serve billions of web documents.
- You thrive in the space between theory and production, where elegant solutions must also run efficiently on real infrastructure.
- You're comfortable reading papers from SIGIR and RecSys one day and debugging distributed training pipelines the next.
- It's on us to ensure real-world outcomes for our customers.
- These are our values:
Visa & Work Authorization
- Visa sponsorships
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This listing is sourced directly from Parallel Systems's careers page and normalized into a canonical job model.